Y. Goto, H. Morita, Y. Shirai, and H. Ichikawa;
“Simulation-based classification in multi-objective optimization problems with social simulation”
You have an ABM
You can generate a pareto front to trade-off multiple objectives
But there are multiple scenarios (prefectures, in this case)
It would be nice if we could draw some insight on the general tradeoffs faced across scenarios
5: Clustering Pareto Fronts
Generate the pareto fronts, then let’s cluster them somehow.
How do you cluster pareto fronts?
Similarity of decision variables
Similarity of model objectives
5: Clustering Pareto Fronts
4: Neural Network/RL and its limits
Thomas Chesney, “A philosophy of intelligent agent-based models”
We can use reinforcement learning with neural networks to teach cars how to behave
4: Neural Network/RL and its limits
For the right amount of sophistication, they eventually learn better behaviour.
The problem is, what does a traffic model with super-smart agents actually means?
ABMs are often not operational puzzles to maximize; agent behaviour needs to be proportional to the problem at hand.
Even though we can, often we shouldn’t.
3: Inverse Generative Social Science
Lux Miranda; “Evolutionary model discovery of human behavioral factors driving decision-making in irrigation experiments”
Public goods game, why do people contribute?
Lab experiment with undergrads
What are their utility functions?
Instead of writing a utility function and calibrating, let’s use genetic programming to evolve the utility function itself.
Inverse Generative Social Science
Inverse Generative Social Science
It works as a gentle introduction to inverse generativve social science.
When the problem and functions are this simple however, IGS is really a “calibration with genetic programming”
2: The dangers of random movement
Edmund Chattoe-Brown , “All the right moves? Systematically exploring the effects of random travel in agent-based models”
to move ; turtle procedure rt random 50 lt random 50 fd 1end
Not the only way to be random; this method produces “line of sight” explorations.
2: The dangers of random movement
to move ; turtle procedure rt random 360 fd 1end
Default Movement
New Movement
“Movement” in the model is a complicated proxy for habitat and exploration. Without estimation/calibration, every component of an abstract model matters.
1. The march of data-assimilation
1. The march of data-assimilation
Alison Heppenstall; “Simulating social systems with individual-based models: are they worth it?”
Deborah Olukan, Jonathan Ward, Nicolas Malleson & Jiaqi Ge; “Heterogeneity in agent-based models”
Joel Dyer; “Black-box Bayesian inference for agent-based models”
